Microsoft's Azure DP-100 Certificate: A Comprehensive Guide

Microsoft's Azure DP-100 Certificate: A Comprehensive Guide
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As a data scientist, staying ahead of the curve and updating your skills is essential to succeed in the field. Microsoft's Azure DP-100 certificate is a great opportunity for data scientists to showcase their expertise in Azure Machine Learning and grow their careers. In this blog post, I'll be sharing my experience of completing the Azure DP-100 certificate, including tips for preparation and what to expect in the exam.

What is Microsoft Azure?

Microsoft Azure is a cloud-based computing platform that provides a wide range of services for data science, including machine learning. It is a powerful platform that enables data scientists to develop and deploy machine learning models quickly and easily. The Azure Machine Learning service provides a fully managed environment for building, deploying, and managing machine learning models. It provides an easy-to-use interface that enables data scientists to get started quickly, without having to worry about the underlying infrastructure.

Why use Azure Machine Learning?

There are several reasons to use Azure Machine Learning. First and foremost, it's easy to use. Azure provides a user-friendly interface that makes it easy to get started, even if you're new to machine learning. The platform also provides a wide range of algorithms, libraries, and tools, making it easy to build models that are optimized for specific use cases.

In addition to its ease of use, Azure also provides robust security features. Azure provides a secure environment for storing and managing data, which is especially important for sensitive data such as financial or personal information. Azure also provides a variety of options for deploying models, including web services and APIs, making it easy to integrate models into existing systems.

Finally, Azure provides scalability and reliability, so data scientists can focus on building models, rather than managing infrastructure. The platform can scale up or down based on demand, so data scientists can be confident that their models will be available when they're needed, even during periods of high traffic.

My thought on Azure Machine Learning (AML)

Working with Azure Machine Learning (AML) has been a great experience for me. It has made the process of building and deploying machine learning models much easier and more streamlined.

One of the standout features of AML is the ability to build and manage end-to-end workflows for machine learning pipelines. With the drag-and-drop interface, it is possible to build complex workflows without needing to write any code. This makes it easy for data scientists and engineers to quickly prototype and deploy models. Additionally, AML also provides the ability to track experiments, monitor and manage models, and deploy models to a variety of deployment targets, including Azure Container Instances, Azure Kubernetes Service, and IoT Edge.

Another key advantage of working with AML is the ability to scale and manage resources efficiently. AML makes it easy to scale your resources up or down based on your needs. For example, when running experiments, you can scale up the compute resources to ensure that your models are trained quickly, and then scale back down when you are done. This makes it possible to optimize the cost of running your experiments, while still having the power you need to get the job done.

One of my personal favorite features of AML is the ability to collaborate and share work with other team members. With AML, it is possible to share work through the use of workspaces, experiments, and models. This makes it easy to collaborate with other data scientists and engineers on your team, and to ensure that everyone is working with the most up-to-date version of your models.

In conclusion, working with Azure Machine Learning has been an extremely positive experience for me. The ease of use, the ability to scale resources, and the ability to collaborate and share work, have all made it a great choice for building and deploying machine learning models. If you are looking for a powerful and flexible platform for machine learning, I would highly recommend giving Azure Machine Learning a try.

Microsoft Data Scientist Certification (DP-100)

The DP-100: Designing and Implementing a Data Science Solution on Azure certification exam consists of 40-60 multiple choice and case study based questions. The exam is timed, with a duration of 120 minutes to complete all the questions.

To pass the DP-100 exam, you need to score a minimum of 700 out of 1000 points, which is equivalent to 70% of the total points. The exam assesses your ability to design, build, and operate machine learning solutions using Azure Machine Learning services.

The Azure DP-100 exam is broken into four sections (updated as of October 18, 2022):

  1. Design and prepare a machine learning solution (20–25%)
  2. Explore data and train models (35–40%)
  3. Prepare a model for deployment (20–25%)
  4. Deploy and retrain a model (10–15%)

Preparation Do’s and Don’ts

Here are some tips that helped me prepare for and pass the DP-100 exam on my first try:


  • Make use of Microsoft’s free DP-100 Learning path. Microsoft provides free resources to help you prepare for the DP-100 exam, including a learning path with 15 modules of high-quality content.
  • Spend time learning the Azure ML SDK for Python. The Azure Machine Learning SDK for Python is a critical component of the exam, so it's essential to understand it well. Reviewing the Azure Machine Learning Labs provided by Microsoft Learn is a great way to do this.
  • Create an Azure Free Trial account to get hands-on experience with Azure ML. Microsoft offers $200 in Azure credits to use during the first 30 days of creating your account. Take advantage of this to try out some of the Azure ML learning labs.


  • Don't use online DP-100 exam dumps. Exam dumps contain real questions from past exams, and they're not accurate representations of the current exam. In addition, accessing exam dumps can result in a lifetime ban from Microsoft certifications.
  • Don't ignore the exam content distributions while studying. The percentages next to each exam content section are important and should guide your focus while studying.
  • Don't to overlook the Automated ML and Designer Visual Tools while preparing for the DP-100 exam. While the majority of the focus should be on the Azure ML SDK for python, make sure to familiarize yourself with Auto ML and Designer as well. Many study plans, including Microsoft's own DP-100 plan, introduce these tools briefly and then concentrate on the python SDK. However, make sure to revise Auto ML and Designer before taking the exam as there may be a few questions specifically about these capabilities.

Passing my DP-100 exam

My Microsoft DP-100 exam experience was a great one. I spent 2 weeks studying for the exam, using the "Build and Operate Machine Learning Solutions with Azure Machine Learning" learning path provided by Microsoft. The learning path includes 15 modules of high-quality content that aligns with the DP-100 exam content, and it provided me with a comprehensive understanding of the concepts required to pass the exam.

The exam consisted of multiple choice and drag-and-drop questions that tested my understanding of various Azure Machine Learning concepts, including the creation of machine learning models, deployment, and management of machine learning solutions. I was also tested on my knowledge of data preparation, model selection, and the evaluation of machine learning models.

Overall, my experience of studying for the DP-100 exam and working with Azure Machine Learning was a great one. The knowledge I gained has been incredibly valuable in my day-to-day work, and I would highly recommend this certification to anyone looking to enhance their skills in the field of machine learning.